The approaches mentioned in [1]} are sophisticated systems where ClusterGAN and MCGAN models are trained using x-vectors extracted from TDNN [2]}.
The embeddings obtained from the models are fused with x-vectors and then clustered using a spectral clustering algorithm.
The number of speakers is estimated with the Normalized Maximum Eigengap (NME-SC) technique [3]}. The NME-SC algorithm is expensive in terms of runtime because it requires iterating through multiple values of the pruning threshold to find the best setting. Therefore, this work estimates the number of speakers with the standard maximum eigengap criterion proposed in [4]}.
We also compare our system with the Variational Bayes (VBx) approach [5]} under the same experimental setup as used in this paper. VBx uses ResNet101-based x-vector embeddings [6]}, [7]} that are clustered using Bayesian Hidden Markov Model (BHMM) [8]}, [7]}, [10]}.
